Sv Method And The Securities And Innovative Business Applications

Financial Market volatility plays a crucial role in measurement of risk, asset pricing and portfolio allocation. However, in practice the volatility is often assumed to be constant, which is not agree with the realistic. At the same time, the Shanghai Stock Exchange (SSE) is dedicating in promoting innovation business, all these business have a close relation with the quantitative analysis of stock volatilities. This paper aims to measure the risk and to price the innovation products under the time-varying volatility. We use the Stochastic Volatility (SV) method, which is more flexible and efficient than the GARCH methods. Considering innovation business are mainly on the SSE 50 stocks, we only model these stocks with three different Stochastic Volatility Models. Using the newly developed software, AD (Auto Differentiation) Model Builder, we give convenient and accurate estimations of the models. The result shows that, except the newly offered stocks, all the other SSE 50 stocks have a notable heavy tail effect but almost no leverage effect, the newly offered stocks, they have an outstanding positive leverage effect, which means that a rise in the price of the stock will promote the volatility. Then, under the credit risk's structural model, we use the SVt model to simulate the default risk of Margin Financing and Securities Lending business on SSE 50 stocks. Finally, we study various methods to price Covered Warrant. For the Monte Carlo method under SV-t model performs not so well, we add the information filtration generated by the warrants market. We introduce a heavy tailed stochastic volatility model expanded with implied volatility of the underlying stock, which we call it SIVt. Under this model, the simulating warrant price fits the market price much better. This has a great importance to warrant's offering.